Modelling Principal Components with Structure

Bernard D. Flury and Beat E. Neuenschwander
Additional contact information
Bernard D. Flury: Indiana University, Department of Mathematics
Beat E. Neuenschwander: Indiana University, Department of Mathematics

Chapter 6 in Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modeling: An Informational Approach, 1994, pp 183-198 from Springer

Abstract: Abstract Applications of principal component analysis often involve data with some predetermine structure. For instal1ce, Jackson (1991, chapter 5) gives an example with eight variables, four being the hearing loss at four different frequencies in the left ear, and the other four the corresponding measurements on the right ear. Another example is the Frets (1921) anthropometric data on head length and breadth of the first and second adult sons of 25 families; see, e.g., Anderson (1984), Izenman (1980). Modelling covariances with constraints imposed according to the structure in the variables has been treated by Szatrowski (1985), Andersson (1975), and others, but the connection to principal component analysis is usually not made.

Keywords: Principal Component Analysis; Hearing Loss; Maximum Likelihood Estimate; Random Vector; Head Length (search for similar items in EconPapers)
Date: 1994
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-94-011-0800-3_7

Ordering information: This item can be ordered from
http://www.springer.com/9789401108003

DOI: 10.1007/978-94-011-0800-3_7

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().